{"as_of":"2026-08-15T04:06:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:65c656130dd49d0f8b5ad9c4b24cf91cec31c8136bfa542bdbdd708813c7c8a2","coverage":[{"denominator":83,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":83,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-02T17:48:30.792529Z","state":"measured"},{"denominator":84,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":84,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-10T06:20:49.175000Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-10T06:26:51.329603Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"cited_work":{"arxiv_id":"2603.21151","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2603.21151","snapshot_observed_at":"2026-07-28T03:23:28.081263Z","title":"Tan, W.-j","venue":null,"work_id":"08c24468-c43e-4017-9402-ac13f839adfc","year":2026},"citing_paper":{"arxiv_id":"2607.08505","last_updated":"2026-07-09T14:00:29Z","snapshot_observed_at":"2026-08-13T00:47:51.926802Z","submitted_at":"2026-07-09T14:00:29Z","title":"Diffusion Models for Sampling Near Criticality in Lattice Field Theories","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-07-10T06:20:49.175000Z"},"links":{"cited_paper":"/paper/2603.21151","citing_paper":"/paper/2607.08505"},"observation_digest":"sha256:259eb32e11196f1a774614e603c59962496d2f3e6e89eea0dd14d755f50e191d","observation_id":"a1faf1c5-f018-452f-bf78-42f44f7de0ae","resolution":{"observed_at":"2026-07-28T03:23:28.081263Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2603.21151/citation-record","integrity":"/paper/2603.21151/integrity","json":"/paper/2603.21151/citation-record.json","paper":"/paper/2603.21151"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T17:48:22.856090Z","title":"We find that the training results are largely insensitive to the choice of learning rate within a reason- able range","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:22.856090Z"},"links":{"citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:0e0a6f36318f7c6df2e38bc59e4a084b48053a874993077f9d77ef2f7f4a5b1c","observation_id":"4f10e439-5a68-45bf-b50d-d46612181008","resolution":{"observed_at":"2026-08-02T17:48:22.856090Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T17:48:22.932870Z","title":"4 presents the field derivative of the rescaled effective potential ˆV ′ k(ˆρ) for theO(4) model atT= 100 MeV, which lies below the critical temperatureT c ≈ 150 MeV","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:22.932870Z"},"links":{"citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:fee88a36701a1d8740381cfc193e479050e15ee2914da4c5efb4686275affe90","observation_id":"30006c6b-23ff-407e-81b7-9e9616756104","resolution":{"observed_at":"2026-08-02T17:48:22.932870Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T17:48:22.990070Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:22.990070Z"},"links":{"citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:cbd6fd12794ea5cc9e140b8cf937408f82715872ada7c74f4097746af7081c64","observation_id":"d091029e-c2a8-40fe-88c7-7fc51d13321c","resolution":{"observed_at":"2026-08-02T17:48:22.990070Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T17:48:23.001190Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:23.001190Z"},"links":{"citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:ac11b02e06933b776b5daccb04fbadbc8069656340c6ec19144e47cf839f0224","observation_id":"190eb908-946e-4be9-a6de-14faf9125277","resolution":{"observed_at":"2026-08-02T17:48:23.001190Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T17:48:23.009886Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:23.009886Z"},"links":{"citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:edc3199ae02820e6a7d02e2ff1838e569c0d10062b40e5b5ffb08fc740ca95c6","observation_id":"5266fb60-917e-462e-b29d-aa2aeaa7a670","resolution":{"observed_at":"2026-08-02T17:48:23.009886Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T17:48:23.043816Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:23.043816Z"},"links":{"citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:5e02d27d41a93a6b251d86a82ee96685905ebf89e857ae487a1fa25772d2637a","observation_id":"683c02f2-e24f-4cd7-92ac-3ef3b6a463c3","resolution":{"observed_at":"2026-08-02T17:48:23.043816Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T17:48:23.075475Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:23.075475Z"},"links":{"citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:956abf918cb9fa063952a8675ddce391c099ef46aa7526f4eaa7fdd026866296","observation_id":"95c2aa78-bbda-431f-9fdf-1d2a4985f15d","resolution":{"observed_at":"2026-08-02T17:48:23.075475Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T17:48:23.094749Z","title":"(A2), is a differential algebraic equa- tion of index 1","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:23.094749Z"},"links":{"citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:063a5327df0a20381396b0ad3d97a35a948c7294993e1e047d88e93671401336","observation_id":"29eeceea-2a6a-4f27-92fc-9af1e55095d1","resolution":{"observed_at":"2026-08-02T17:48:23.094749Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T17:48:23.127595Z","title":"Wetterich, Exact evolution equation for the effective potential, Phys","venue":null,"work_id":null,"year":1993},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:23.127595Z"},"links":{"citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:9c4c59107de7a394994d36dad6c05688d0cf4ac5312425949921561ff36ea760","observation_id":"54975864-8f64-4595-80db-440dc3150fb4","resolution":{"observed_at":"2026-08-02T17:48:23.127595Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T17:48:23.155053Z","title":null,"venue":null,"work_id":null,"year":1971},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:23.155053Z"},"links":{"citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:4c4d9e700c4eb3873cfaf44e9d59af582137a5779a6e01b90825470292afb856","observation_id":"d300781e-e276-4715-81d1-f2bd96e0da17","resolution":{"observed_at":"2026-08-02T17:48:23.155053Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T17:48:23.197684Z","title":null,"venue":null,"work_id":null,"year":1971},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:23.197684Z"},"links":{"citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:5754fd4ccde10d7842f7db5017368238c5c10d5120ca1c96ff42d58ee237de06","observation_id":"eb163b07-cbf2-4851-a75f-2be4f9f6a352","resolution":{"observed_at":"2026-08-02T17:48:23.197684Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T17:48:23.222607Z","title":null,"venue":null,"work_id":null,"year":1972},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:23.222607Z"},"links":{"citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:4de3f1b0bba6260a0e6d86c92fa63ae9a2920ef48a20e58f10ba3d3182bfb1a5","observation_id":"0f608116-c292-4638-a2a0-8cbd8d2d9bd3","resolution":{"observed_at":"2026-08-02T17:48:23.222607Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T17:48:23.257210Z","title":null,"venue":null,"work_id":null,"year":1974},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:23.257210Z"},"links":{"citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:bac3f829a517b33877cfef098bf64a0a25e909e0263ea257d285b3dbf45955ed","observation_id":"81ff003f-b414-462a-a825-91be50df368f","resolution":{"observed_at":"2026-08-02T17:48:23.257210Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T17:48:23.286615Z","title":"Berges, N","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:23.286615Z"},"links":{"citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:2861e3da35a593d6f4af2a29b7ede7860b79685bc1a1bb434212ede24ca51b8b","observation_id":"bb9de32a-38d2-4a54-9f0c-2f8a00f3fda7","resolution":{"observed_at":"2026-08-02T17:48:23.286615Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T17:48:23.349587Z","title":"Canet and H","venue":null,"work_id":null,"year":1937},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:23.349587Z"},"links":{"citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:6809e994a3da72b7662451aa4b605be96d3d2c05bfd31bf01e9b7c62102a324f","observation_id":"f6a73a8b-88e5-4dfc-9295-8746cbebd7cb","resolution":{"observed_at":"2026-08-02T17:48:23.349587Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1106.4129","last_updated":"2011-11-18T10:07:33Z","snapshot_observed_at":"2026-08-08T12:17:33.811597Z","submitted_at":"2011-06-21T08:34:25Z","title":"General framework of the non-perturbative renormalization group for non-equilibrium steady states","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1106.4129","snapshot_observed_at":"2026-08-02T17:48:23.380102Z","title":"Canet, H","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:23.380102Z"},"links":{"cited_paper":"/paper/1106.4129","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:e44eab6d043ac648907135b43725d0b94f4f36a7535aa2c271714ca6cc992273","observation_id":"9b1d9a20-d76d-4a27-b426-df94339a3da7","resolution":{"observed_at":"2026-08-02T17:48:23.380102Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1307.1700","last_updated":"2013-11-04T17:18:19Z","snapshot_observed_at":"2026-08-15T00:11:36.744042Z","submitted_at":"2013-07-05T20:00:01Z","title":"Dynamic universality class of Model C from the functional renormalization group","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1307.1700","snapshot_observed_at":"2026-08-02T17:48:23.424645Z","title":"Mesterh´ azy, J","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:23.424645Z"},"links":{"cited_paper":"/paper/1307.1700","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:3082d3f347affd1eedc8108540cce2c0e2ae307a357b4c8f2d0ffad8f4fd2bff","observation_id":"588a156b-eac6-4166-9b21-c0a5fd63ac84","resolution":{"observed_at":"2026-08-02T17:48:23.424645Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1808.01377","last_updated":"2018-08-03T22:17:04Z","snapshot_observed_at":"2026-08-14T18:44:37.220116Z","submitted_at":"2018-08-03T22:17:04Z","title":"Time-evolution of fluctuations as signal of the phase transition dynamics in a QCD-assisted transport approach","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1808.01377","snapshot_observed_at":"2026-08-02T17:48:23.495076Z","title":"Bluhm, Y","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:23.495076Z"},"links":{"cited_paper":"/paper/1808.01377","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:1d979cde7814e65051f7d34dc8769578d16cf4d2e60b8ba9f1d273eea1f1a7ed","observation_id":"17fd54f5-9e61-464e-b03a-7b019712e631","resolution":{"observed_at":"2026-08-02T17:48:23.495076Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.11817","last_updated":"2023-03-21T13:00:40Z","snapshot_observed_at":"2026-08-13T12:19:59.388926Z","submitted_at":"2023-03-21T13:00:40Z","title":"Critical dynamics in a real-time formulation of the functional renormalization group","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.11817","snapshot_observed_at":"2026-08-02T17:48:23.636385Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:23.636385Z"},"links":{"cited_paper":"/paper/2303.11817","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:2baee76bb47383d8da29b8b5504a184afc009a9c56ae7c6b86dd4a58f05b1fc9","observation_id":"e8c590b1-f202-4a7d-8ec0-01f8b844373c","resolution":{"observed_at":"2026-08-02T17:48:23.636385Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.03503","last_updated":"2025-03-23T05:49:10Z","snapshot_observed_at":"2026-08-13T04:02:29.461659Z","submitted_at":"2024-03-06T07:19:36Z","title":"Universality of pseudo-Goldstone damping near critical points","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.03503","snapshot_observed_at":"2026-08-02T17:48:23.767889Z","title":"Tan, Y.-r","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:23.767889Z"},"links":{"cited_paper":"/paper/2403.03503","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:aa0e8946ccbdf7253623c5a9147173e9eddf7866a8074717168df8e7fdb2a93c","observation_id":"2102c9b4-f081-4a55-8d7f-19655620a964","resolution":{"observed_at":"2026-08-02T17:48:23.767889Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.00679","last_updated":"2024-10-07T12:31:18Z","snapshot_observed_at":"2026-08-12T23:52:15.836020Z","submitted_at":"2024-06-02T09:19:44Z","title":"Critical dynamics of Model H within the real-time fRG approach","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.00679","snapshot_observed_at":"2026-08-02T17:48:23.810562Z","title":"Chen, Y.-y","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:23.810562Z"},"links":{"cited_paper":"/paper/2406.00679","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:7abf4a63dbd6fe0cf9ada458d921ef641b073b4cb7ed6567f30e2fcc791398b3","observation_id":"32907af1-0e75-47dc-986c-6dbdc44d939a","resolution":{"observed_at":"2026-08-02T17:48:23.810562Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.04573","last_updated":"2024-03-07T15:15:29Z","snapshot_observed_at":"2026-08-13T01:00:02.496616Z","submitted_at":"2024-03-07T15:15:29Z","title":"Dynamic critical behavior of the chiral phase transition from the real-time functional renormalization group","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.04573","snapshot_observed_at":"2026-08-02T17:48:23.904303Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:23.904303Z"},"links":{"cited_paper":"/paper/2403.04573","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:16c11b2e25f70610676ccb590b3d89f4d1565189662c581e4bd0511df6e098ea","observation_id":"53111f44-5d7b-411a-91aa-b2aefedf7e1e","resolution":{"observed_at":"2026-08-02T17:48:23.904303Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.14470","last_updated":"2025-02-26T10:08:29Z","snapshot_observed_at":"2026-08-13T18:04:55.529866Z","submitted_at":"2024-09-22T14:39:22Z","title":"Universal critical dynamics near the chiral phase transition and the QCD critical point","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.14470","snapshot_observed_at":"2026-08-02T17:48:24.033435Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:24.033435Z"},"links":{"cited_paper":"/paper/2409.14470","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:24f3efa327eff669cb4a38b20aac95e50edbf6a5a3a1bbf9bc4d74f919f83423","observation_id":"7ca78e76-84d9-41bc-ac07-f69409661377","resolution":{"observed_at":"2026-08-02T17:48:24.033435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.1045","last_updated":"2016-07-22T13:38:31Z","snapshot_observed_at":"2026-08-14T23:09:15.133402Z","submitted_at":"2014-12-02T20:01:39Z","title":"From Quarks and Gluons to Hadrons: Chiral Symmetry Breaking in Dynamical QCD","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.1045","snapshot_observed_at":"2026-08-02T17:48:24.102149Z","title":"Braun, L","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:24.102149Z"},"links":{"cited_paper":"/paper/1412.1045","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:0c3b968bd4f40dd82822915030a4f6975a2649017b8cbaa27ddd99a34dd681ea","observation_id":"43ed2b59-8bbc-44a9-8b96-1cbd806d0841","resolution":{"observed_at":"2026-08-02T17:48:24.102149Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1411.7978","last_updated":"2014-12-18T16:53:43Z","snapshot_observed_at":"2026-08-14T23:09:46.618000Z","submitted_at":"2014-11-28T19:09:38Z","title":"Chiral symmetry breaking in continuum QCD","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1411.7978","snapshot_observed_at":"2026-08-02T17:48:24.139539Z","title":"Mitter, J","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:24.139539Z"},"links":{"cited_paper":"/paper/1411.7978","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:4e7babc685f05fc5b1b8c0130b6ad7b22e5228df3325e9eba9708a37ea0110c0","observation_id":"98a5bd01-0231-4fe4-8d7a-3d71a35c7d73","resolution":{"observed_at":"2026-08-02T17:48:24.139539Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1504.03585","last_updated":"2015-04-14T15:28:37Z","snapshot_observed_at":"2026-08-14T22:52:29.181859Z","submitted_at":"2015-04-14T15:28:37Z","title":"The Vacuum Structure of Vector Mesons in QCD","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1504.03585","snapshot_observed_at":"2026-08-02T17:48:24.213858Z","title":"Rennecke, Vacuum structure of vector mesons in QCD, Phys","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:24.213858Z"},"links":{"cited_paper":"/paper/1504.03585","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:6b41eed26438ad77b6e060df8372b1eed52bcb1d59e21771e5f0f0c4271c3b99","observation_id":"ff70012b-b263-41ce-be73-39c5bef69e1a","resolution":{"observed_at":"2026-08-02T17:48:24.213858Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1605.01856","last_updated":"2016-09-09T16:31:22Z","snapshot_observed_at":"2026-08-14T21:58:35.089949Z","submitted_at":"2016-05-06T08:18:16Z","title":"Landau gauge Yang-Mills correlation functions","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1605.01856","snapshot_observed_at":"2026-08-02T17:48:24.315371Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:24.315371Z"},"links":{"cited_paper":"/paper/1605.01856","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:c23128374c12639be8784ca69ed7b6011b625497a9a711a024c131d4d73a7725","observation_id":"7f284a52-61f3-477b-921b-4c938fa15dcb","resolution":{"observed_at":"2026-08-02T17:48:24.315371Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1708.03482","last_updated":"2018-03-17T16:11:41Z","snapshot_observed_at":"2026-08-14T20:41:56.840239Z","submitted_at":"2017-08-11T09:26:51Z","title":"Non-perturbative finite-temperature Yang-Mills theory","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1708.03482","snapshot_observed_at":"2026-08-02T17:48:24.479790Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:24.479790Z"},"links":{"cited_paper":"/paper/1708.03482","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:c242455ebf7fa552b0594b305c2b8a9f5695ce61a4cf4a119a9537b178102b7d","observation_id":"bde2be11-8be3-4fe9-af11-e347f076f950","resolution":{"observed_at":"2026-08-02T17:48:24.479790Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1706.06326","last_updated":"2017-06-20T09:03:29Z","snapshot_observed_at":"2026-08-14T20:52:54.838024Z","submitted_at":"2017-06-20T09:03:29Z","title":"Non-perturbative quark, gluon and meson correlators of unquenched QCD","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.06326","snapshot_observed_at":"2026-08-02T17:48:24.648573Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:24.648573Z"},"links":{"cited_paper":"/paper/1706.06326","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:f899cd2cab494b07fde592537684b97a527b3da9149758656c7fdd65abc942e3","observation_id":"d992b0e3-a449-4ea7-ae78-ea59d3b3afd9","resolution":{"observed_at":"2026-08-02T17:48:24.648573Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1909.02991","last_updated":"2019-09-06T16:10:34Z","snapshot_observed_at":"2026-08-10T17:18:41.068180Z","submitted_at":"2019-09-06T16:10:34Z","title":"The QCD phase structure at finite temperature and density","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.02991","snapshot_observed_at":"2026-08-02T17:48:24.783410Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:24.783410Z"},"links":{"cited_paper":"/paper/1909.02991","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:3c4fd3f1b26796e63d4da4b496a9ef17852c6f3fa50d52989e8574ae25b16055","observation_id":"9a7daab6-b403-48d0-8d97-990959f68176","resolution":{"observed_at":"2026-08-02T17:48:24.783410Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2003.13112","last_updated":"2020-03-29T19:06:52Z","snapshot_observed_at":"2026-08-11T02:22:37.077944Z","submitted_at":"2020-03-29T19:06:52Z","title":"Chiral Susceptibility in (2+1)-flavour QCD","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.13112","snapshot_observed_at":"2026-08-02T17:48:24.959845Z","title":"Braun, W.-j","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:24.959845Z"},"links":{"cited_paper":"/paper/2003.13112","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:9bac90fc01403b1921c810bfb954d1a38dc6e97d33c35bae449a862c1e410d08","observation_id":"5ead5562-59a2-45cb-9389-178a4e5f45d6","resolution":{"observed_at":"2026-08-02T17:48:24.959845Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.08413","last_updated":"2024-08-15T20:40:49Z","snapshot_observed_at":"2026-08-12T23:02:55.703000Z","submitted_at":"2024-08-15T20:40:49Z","title":"Towards quantitative precision in functional QCD I","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.08413","snapshot_observed_at":"2026-08-02T17:48:25.089579Z","title":"Ihssen, J","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:25.089579Z"},"links":{"cited_paper":"/paper/2408.08413","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:94ff068caca73f56deb133a7d275eb5201f03fd70ee8f6c1756611f0e67faf3d","observation_id":"717d2fe8-16ef-40f0-835a-06964893e8f0","resolution":{"observed_at":"2026-08-02T17:48:25.089579Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T17:48:25.269613Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:25.269613Z"},"links":{"citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:887dec9b2d5b6de9767628d121384450f97538c6661d99b8c23ae7918d2f8589","observation_id":"e8f05860-4af9-43b4-bf9b-6204587f0f07","resolution":{"observed_at":"2026-08-02T17:48:25.269613Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T17:48:25.415321Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:25.415321Z"},"links":{"citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:a8ee5927c4f05ce978f298a9f51bc674ebc6ec78a4c54cf3bb35cc762947a30c","observation_id":"28946e1d-6441-4ccf-aab0-887d968ccbdf","resolution":{"observed_at":"2026-08-02T17:48:25.415321Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T17:48:25.562618Z","title":null,"venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:25.562618Z"},"links":{"citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:e3938f04560b950401e04002de016fe7b588d5859b04162bccd38669461ae1e8","observation_id":"be78d33e-6818-4a69-b4e9-05fa2aed05cd","resolution":{"observed_at":"2026-08-02T17:48:25.562618Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1108.4449","last_updated":"2011-08-22T21:47:04Z","snapshot_observed_at":"2026-07-30T21:32:07.504330Z","submitted_at":"2011-08-22T21:47:04Z","title":"Fermion Interactions and Universal Behavior in Strongly Interacting Theories","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1108.4449","snapshot_observed_at":"2026-08-02T17:48:25.718097Z","title":"Braun, Fermion Interactions and Universal Behavior in Strongly Interacting Theories, J","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:25.718097Z"},"links":{"cited_paper":"/paper/1108.4449","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:8165e0ef2c429ade90f89ee89481a8740144cac333476c17151140a5194a7f75","observation_id":"057fb33c-9dcb-4a3b-ae95-18256e04b67f","resolution":{"observed_at":"2026-08-02T17:48:25.718097Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04853","last_updated":"2021-05-07T08:46:33Z","snapshot_observed_at":"2026-08-11T10:23:39.719186Z","submitted_at":"2020-06-08T18:17:41Z","title":"The nonperturbative functional renormalization group and its applications","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.04853","snapshot_observed_at":"2026-08-02T17:48:25.873100Z","title":"Dupuis, L","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:25.873100Z"},"links":{"cited_paper":"/paper/2006.04853","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:312ab19b583df1d3de014305e8851dc5f773300e536e5c0f95578df401082923","observation_id":"ba3d457a-dab7-4a11-b85b-e9a4947949f8","resolution":{"observed_at":"2026-08-02T17:48:25.873100Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.00468","last_updated":"2022-05-01T13:51:31Z","snapshot_observed_at":"2026-08-13T15:52:33.537139Z","submitted_at":"2022-05-01T13:51:31Z","title":"QCD at finite temperature and density within the fRG approach: An overview","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.00468","snapshot_observed_at":"2026-08-02T17:48:25.983069Z","title":"Fu, QCD at finite temperature and density within the fRG approach: an overview, Commun","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:25.983069Z"},"links":{"cited_paper":"/paper/2205.00468","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:37dd43ab257b7242a3b838a9caefdb07f9db42dd0301f908a2481c42472d89af","observation_id":"89b374e0-086f-4155-9b08-bfd69f2b098e","resolution":{"observed_at":"2026-08-02T17:48:25.983069Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.02309","last_updated":"2022-05-02T16:59:29Z","snapshot_observed_at":"2026-08-14T05:05:45.117943Z","submitted_at":"2021-12-04T11:26:00Z","title":"Machine Learning in Nuclear Physics","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.02309","snapshot_observed_at":"2026-08-02T17:48:26.074366Z","title":"Boehnleinet al., Colloquium: Machine learning in nuclear physics, Rev","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:26.074366Z"},"links":{"cited_paper":"/paper/2112.02309","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:57006e2c6bb33c6acb7f3f549467cee2a0b6a94eafce36e3418387032bf9f2dd","observation_id":"eb16896d-f79b-4bfc-ad3d-d3d9be377561","resolution":{"observed_at":"2026-08-02T17:48:26.074366Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.15136","last_updated":"2023-12-01T23:47:32Z","snapshot_observed_at":"2026-08-13T12:15:44.198877Z","submitted_at":"2023-03-27T12:10:42Z","title":"Exploring QCD matter in extreme conditions with Machine Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.15136","snapshot_observed_at":"2026-08-02T17:48:26.210251Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:26.210251Z"},"links":{"cited_paper":"/paper/2303.15136","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:fb99640b42ac3e36b7b7246e202d856aedc1bb20791756063b500973f4fa402d","observation_id":"97c2f504-e72e-4336-afda-8e328e78a568","resolution":{"observed_at":"2026-08-02T17:48:26.210251Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.05580","last_updated":"2025-01-09T21:14:25Z","snapshot_observed_at":"2026-08-11T03:26:26.156370Z","submitted_at":"2025-01-09T21:14:25Z","title":"Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.05580","snapshot_observed_at":"2026-08-02T17:48:26.295632Z","title":"Aarts, K","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:26.295632Z"},"links":{"cited_paper":"/paper/2501.05580","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:3894ae16c6725292bdb83517f15f00cbca1cb3c1d6b184b5b682d9de7056f4f2","observation_id":"f44428a9-0f95-4611-9c32-b97001f489db","resolution":{"observed_at":"2026-08-02T17:48:26.295632Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2202.05838","last_updated":"2022-02-10T22:59:40Z","snapshot_observed_at":"2026-08-13T21:27:40.064750Z","submitted_at":"2022-02-10T22:59:40Z","title":"Applications of Machine Learning to Lattice Quantum Field Theory","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.05838","snapshot_observed_at":"2026-08-02T17:48:26.444526Z","title":"Boydaet al., Applications of Machine Learning to Lat- tice Quantum Field Theory, inSnowmass 2021(2022) arXiv:2202.05838 [hep-lat]","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:26.444526Z"},"links":{"cited_paper":"/paper/2202.05838","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:6c7efebabd1f65f9d5b489bd9d0cc5549421e7a0e39f53b3e0010b530712b1c9","observation_id":"a08f22c7-bcb5-4b1f-80f7-9ee849294518","resolution":{"observed_at":"2026-08-02T17:48:26.444526Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.01156","last_updated":"2023-09-03T12:25:59Z","snapshot_observed_at":"2026-08-13T10:21:55.861840Z","submitted_at":"2023-09-03T12:25:59Z","title":"Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.01156","snapshot_observed_at":"2026-08-02T17:48:26.571922Z","title":"Cranmer, G","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:26.571922Z"},"links":{"cited_paper":"/paper/2309.01156","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:7166337815c5624f8cf0cdbfe5911dadbb875aa9a865d3a5c3d8e8ff3a08cddd","observation_id":"325230c5-2261-40cb-8e95-289b05944ac5","resolution":{"observed_at":"2026-08-02T17:48:26.571922Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.17082","last_updated":"2024-05-09T00:56:24Z","snapshot_observed_at":"2026-08-13T10:02:27.771944Z","submitted_at":"2023-09-29T09:26:59Z","title":"Diffusion Models as Stochastic Quantization in Lattice Field Theory","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.17082","snapshot_observed_at":"2026-08-02T17:48:26.621702Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:26.621702Z"},"links":{"cited_paper":"/paper/2309.17082","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:02c795f36efd86377010b2c26172dccdc717a1e20dcc9f4bf6df0121ec6d4661","observation_id":"a88d3111-5287-4088-945c-51321d469080","resolution":{"observed_at":"2026-08-02T17:48:26.621702Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.03578","last_updated":"2023-11-06T22:24:28Z","snapshot_observed_at":"2026-08-13T05:31:04.858797Z","submitted_at":"2023-11-06T22:24:28Z","title":"Generative Diffusion Models for Lattice Field Theory","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.03578","snapshot_observed_at":"2026-08-02T17:48:26.669330Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:26.669330Z"},"links":{"cited_paper":"/paper/2311.03578","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:edc1d66717eec909df800f26e27c38078b0334ffabb905ccd807d59565c8aa85","observation_id":"ec04b4fa-7734-4dfc-9230-909d42805b9c","resolution":{"observed_at":"2026-08-02T17:48:26.669330Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.19602","last_updated":"2024-10-25T14:49:20Z","snapshot_observed_at":"2026-08-15T02:18:15.257335Z","submitted_at":"2024-10-25T14:49:20Z","title":"Diffusion models for lattice gauge field simulations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.19602","snapshot_observed_at":"2026-08-02T17:48:26.777194Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:26.777194Z"},"links":{"cited_paper":"/paper/2410.19602","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:0a3b990de081b7c42ce44e854b99aff44fb9d05ce5f39fb7373676ac6ed4207c","observation_id":"cae84b11-cefc-46ed-92ea-49135756fc7d","resolution":{"observed_at":"2026-08-02T17:48:26.777194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T17:48:26.862052Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:26.862052Z"},"links":{"citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:5634cbc4248da2b9b11c18ecc7ed63e9159c988010102e840b3fccd7ed45a794","observation_id":"91d377fd-f05a-4024-9cbf-22a51587626a","resolution":{"observed_at":"2026-08-02T17:48:26.862052Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T17:48:26.994023Z","title":"Aarts, D","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:26.994023Z"},"links":{"citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:309f137431f1bf20e6ee3c6165b115b2d8659de4fd0b59bdbfebc9e202c2eba3","observation_id":"62783d91-d8a7-4fe2-8307-d69482d26a38","resolution":{"observed_at":"2026-08-02T17:48:26.994023Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T17:48:27.135582Z","title":"Raissi, P","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:27.135582Z"},"links":{"citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:a08a9cd6f1b826cabb14f221c0f21a557a15cb6d2e3c6570ffe96b978d34a68d","observation_id":"452b4449-8a0e-4fd2-8490-3ca910acc514","resolution":{"observed_at":"2026-08-02T17:48:27.135582Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T17:48:27.319765Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:27.319765Z"},"links":{"citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:3b083e852e94cfe3aa77f173add1cc1e1017c44c7207a02426ebfcb0d40069c3","observation_id":"1051b436-6521-4c17-94dd-2fa7baa890be","resolution":{"observed_at":"2026-08-02T17:48:27.319765Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.16038","last_updated":"2024-06-14T00:44:26Z","snapshot_observed_at":"2026-08-13T04:53:51.470663Z","submitted_at":"2023-12-26T12:55:36Z","title":"Physics-informed neural networks for solving functional renormalization group on a lattice","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.16038","snapshot_observed_at":"2026-08-02T17:48:27.481996Z","title":"Yokota, Physics-informed neural networks for solving functional renormalization group on a lattice, Phys","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:27.481996Z"},"links":{"cited_paper":"/paper/2312.16038","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:4700724a14e20bdbc13de1bfb24b16703a0b8c5a230ec6e3a9e7833153fc81b6","observation_id":"4a9e97bc-4891-4a79-9805-13d4bd4dd22c","resolution":{"observed_at":"2026-08-02T17:48:27.481996Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.18153","last_updated":"2024-10-23T06:16:35Z","snapshot_observed_at":"2026-08-12T22:17:01.780402Z","submitted_at":"2024-10-23T06:16:35Z","title":"Physics-informed Neural Networks for Functional Differential Equations: Cylindrical Approximation and Its Convergence Guarantees","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.18153","snapshot_observed_at":"2026-08-02T17:48:27.574309Z","title":"Miyagawa and T","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:27.574309Z"},"links":{"cited_paper":"/paper/2410.18153","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:8599a39ae381076fb11c8e863cc4631c89be509e105ffed9c8bfe3832f117c4c","observation_id":"7f03908e-0b7d-4701-8ba2-8abdac9cad40","resolution":{"observed_at":"2026-08-02T17:48:27.574309Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.02177","last_updated":"2025-05-13T13:01:29Z","snapshot_observed_at":"2026-08-12T22:08:11.781159Z","submitted_at":"2024-11-04T15:36:17Z","title":"Physics-informed neural networks viewpoint for solving the Dyson-Schwinger equations of quantum electrodynamics","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.02177","snapshot_observed_at":"2026-08-02T17:48:27.715562Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:27.715562Z"},"links":{"cited_paper":"/paper/2411.02177","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:9fae85ce5767761631e8fca3b69b77735392ce437f424b64021797812ae7e450","observation_id":"e6b6fe95-eef8-4305-87cb-bae7300f8849","resolution":{"observed_at":"2026-08-02T17:48:27.715562Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2510.24728","last_updated":"2026-07-23T13:56:52Z","snapshot_observed_at":"2026-08-11T13:58:08.425421Z","submitted_at":"2025-10-06T13:19:17Z","title":"Spectral functions in Minkowski quantum electrodynamics from neural reconstruction","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2510.24728","snapshot_observed_at":"2026-08-02T17:48:27.870358Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:27.870358Z"},"links":{"cited_paper":"/paper/2510.24728","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:f8835d97a8ca760fd0dd4562d2fc2b546930ad2f0e3ff08537d7b6d9b87cbbcb","observation_id":"c261d2fc-e45c-42c1-9bdc-3932dc8f2718","resolution":{"observed_at":"2026-08-02T17:48:27.870358Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T17:48:27.983376Z","title":null,"venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:27.983376Z"},"links":{"citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:9b906b04c2688e9c09017f08fde7654546a7bf4e472a38a9feb5ec355eec95cb","observation_id":"50ee5e39-04a1-4144-b7ae-86f67e195499","resolution":{"observed_at":"2026-08-02T17:48:27.983376Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"hep-th/0103195","last_updated":"2001-08-03T16:47:00Z","snapshot_observed_at":"2026-07-07T04:39:30.623701Z","submitted_at":"2001-03-22T20:08:54Z","title":"Optimised Renormalisation Group Flows","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"hep-th/0103195","snapshot_observed_at":"2026-08-02T17:48:28.129039Z","title":null,"venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:28.129039Z"},"links":{"cited_paper":"/paper/hep-th/0103195","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:ba0f49c257976fcccad8acdd8e92e7a6452e3d31d9fe7e7cf9b0fc3dcc68799d","observation_id":"7cf8450f-98a8-428e-b5c0-170ef7f20287","resolution":{"observed_at":"2026-08-02T17:48:28.129039Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1512.03598","last_updated":"2015-12-11T11:04:14Z","snapshot_observed_at":"2026-08-14T22:18:59.648632Z","submitted_at":"2015-12-11T11:04:14Z","title":"Physics and the choice of regulators in functional renormalisation group flows","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1512.03598","snapshot_observed_at":"2026-08-02T17:48:28.213253Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:28.213253Z"},"links":{"cited_paper":"/paper/1512.03598","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:34066d481d0a75158461ebc368b8859528bca17495d3c7baa650d3de498cfd08","observation_id":"f40a4420-dc27-4137-8cde-94cf901d1475","resolution":{"observed_at":"2026-08-02T17:48:28.213253Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1907.01829","last_updated":"2019-07-03T10:11:05Z","snapshot_observed_at":"2026-08-14T16:09:12.774753Z","submitted_at":"2019-07-03T10:11:05Z","title":"Convergence of Non-Perturbative Approximations to the Renormalization Group","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.01829","snapshot_observed_at":"2026-08-02T17:48:28.311532Z","title":"Balog, H","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:28.311532Z"},"links":{"cited_paper":"/paper/1907.01829","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:ceb0b6adbbde60bd401965ab869e8c422890743d14118c4f115d970312f71509","observation_id":"eb91ed72-33df-40fa-9aee-15b940b35982","resolution":{"observed_at":"2026-08-02T17:48:28.311532Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.07525","last_updated":"2020-01-21T13:40:37Z","snapshot_observed_at":"2026-08-09T20:15:08.434927Z","submitted_at":"2020-01-21T13:40:37Z","title":"Precision calculation of critical exponents in the $O(N)$ universality classes with the nonperturbative renormalization group","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.07525","snapshot_observed_at":"2026-08-02T17:48:28.449729Z","title":"De Polsi, I","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:28.449729Z"},"links":{"cited_paper":"/paper/2001.07525","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:bd56a4bb037c965dbae7a27e1c90aedaec0ae31b395b6149ab3093063c8e8f7d","observation_id":"217c44b4-39ca-47da-b83c-20be21500dc6","resolution":{"observed_at":"2026-08-02T17:48:28.449729Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"hep-th/0203006","last_updated":"2002-03-18T14:54:18Z","snapshot_observed_at":"2026-07-07T04:42:45.412439Z","submitted_at":"2002-03-01T05:17:24Z","title":"Critical exponents from optimised renormalisation group flows","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"hep-th/0203006","snapshot_observed_at":"2026-08-02T17:48:28.580413Z","title":null,"venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:28.580413Z"},"links":{"cited_paper":"/paper/hep-th/0203006","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:46830f09da6c0ac404e0f2536e2d0ae917847e1424672778c10fb515292947cd","observation_id":"587184f5-e084-423b-8084-93fa72d99f42","resolution":{"observed_at":"2026-08-02T17:48:28.580413Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1502.07511","last_updated":"2015-09-02T12:32:04Z","snapshot_observed_at":"2026-08-14T22:58:33.429949Z","submitted_at":"2015-02-26T11:33:26Z","title":"Global solutions of functional fixed point equations via pseudo-spectral methods","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1502.07511","snapshot_observed_at":"2026-08-02T17:48:28.690837Z","title":"Borchardt and B","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:28.690837Z"},"links":{"cited_paper":"/paper/1502.07511","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:9b1c903b7ecbdc50f3d60b4fdf502a5452f5f7f402756a0bcdf710d933770e78","observation_id":"30589026-6e00-4dc9-9106-4876a3ee5265","resolution":{"observed_at":"2026-08-02T17:48:28.690837Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1603.06726","last_updated":"2016-03-22T10:34:32Z","snapshot_observed_at":"2026-08-14T22:05:01.734603Z","submitted_at":"2016-03-22T10:34:32Z","title":"Solving functional flow equations with pseudo-spectral methods","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1603.06726","snapshot_observed_at":"2026-08-02T17:48:28.846111Z","title":"Borchardt and B","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:28.846111Z"},"links":{"cited_paper":"/paper/1603.06726","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:a2ab25cee43d247a267aa3c656cae0d59b353f80b1a44f2f7ad59ba37b9ff091","observation_id":"a9d700a4-2220-4136-b264-67c2f108f95a","resolution":{"observed_at":"2026-08-02T17:48:28.846111Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2101.08484","last_updated":"2021-01-21T07:49:34Z","snapshot_observed_at":"2026-08-03T20:56:41.102237Z","submitted_at":"2021-01-21T07:49:34Z","title":"Critical behaviors of the $O(4)$ and $Z(2)$ symmetries in the QCD phase diagram","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2101.08484","snapshot_observed_at":"2026-08-02T17:48:28.937409Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:28.937409Z"},"links":{"cited_paper":"/paper/2101.08484","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:02de0f2ceb77a7c0038125eaae170c089a0fe311cddd5079e579f151a7715a4b","observation_id":"a8938dbb-45c8-4456-9d9f-80a261f55d11","resolution":{"observed_at":"2026-08-02T17:48:28.937409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1903.09503","last_updated":"2022-11-15T19:02:20Z","snapshot_observed_at":"2026-08-14T16:58:14.756793Z","submitted_at":"2019-03-22T13:35:45Z","title":"Resolving phase transitions with Discontinuous Galerkin methods","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1903.09503","snapshot_observed_at":"2026-08-02T17:48:29.033643Z","title":"Grossi and N","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:29.033643Z"},"links":{"cited_paper":"/paper/1903.09503","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:c92d96fc495e326def8292f3f788296873d54af393820a752a13cc1ae7de0c79","observation_id":"fcdf4f6c-633f-4611-92ee-6912c8e241c2","resolution":{"observed_at":"2026-08-02T17:48:29.033643Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.13043","last_updated":"2024-12-17T16:08:06Z","snapshot_observed_at":"2026-08-14T16:07:47.044757Z","submitted_at":"2024-12-17T16:08:06Z","title":"DiFfRG: A Discretisation Framework for functional Renormalisation Group flows","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.13043","snapshot_observed_at":"2026-08-02T17:48:29.141283Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:29.141283Z"},"links":{"cited_paper":"/paper/2412.13043","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:c5c85c0e0c1ddafa703dc115ade7d9bf9f23ca884078864c647fe7faedf3d8a9","observation_id":"07b5f7ea-d9da-4acf-a288-c25888a05ad5","resolution":{"observed_at":"2026-08-02T17:48:29.141283Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.16053","last_updated":"2024-12-20T16:53:43Z","snapshot_observed_at":"2026-08-15T02:42:08.209404Z","submitted_at":"2024-12-20T16:53:43Z","title":"Functional Renormalization Group meets Computational Fluid Dynamics: RG flows in a multi-dimensional field space","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.16053","snapshot_observed_at":"2026-08-02T17:48:29.243392Z","title":"Zorbach, A","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:29.243392Z"},"links":{"cited_paper":"/paper/2412.16053","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:bcfc6d5d4e3df67e838a5b0fc624ae596ed543e392a0bf4e9da73db6546458ed","observation_id":"130b005d-2e41-4d01-9b5c-87110fe7ee11","resolution":{"observed_at":"2026-08-02T17:48:29.243392Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.13120","last_updated":"2022-12-01T06:10:40Z","snapshot_observed_at":"2026-08-13T14:18:47.630832Z","submitted_at":"2022-09-27T02:27:47Z","title":"Four-quark scatterings in QCD I","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.13120","snapshot_observed_at":"2026-08-02T17:48:29.330626Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:29.330626Z"},"links":{"cited_paper":"/paper/2209.13120","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:0e0e45025170de1d766da11c6a9c4f627bb2346ec29595bc5c94065ee8e409a6","observation_id":"448c9512-9fd2-4830-81f6-739f80144f97","resolution":{"observed_at":"2026-08-02T17:48:29.330626Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.14388","last_updated":"2025-02-20T09:23:13Z","snapshot_observed_at":"2026-08-07T18:02:33.662725Z","submitted_at":"2025-02-20T09:23:13Z","title":"Four-quark scatterings in QCD III","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.14388","snapshot_observed_at":"2026-08-02T17:48:29.384162Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:29.384162Z"},"links":{"cited_paper":"/paper/2502.14388","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:4c20176b8194c988218e523097cbacd6b897df2716b48e897779df0116f9cd3f","observation_id":"67f7bbe3-e255-4451-9ab9-af8962eb8d16","resolution":{"observed_at":"2026-08-02T17:48:29.384162Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2107.06482","last_updated":"2021-10-19T13:49:34Z","snapshot_observed_at":"2026-08-14T17:44:13.311723Z","submitted_at":"2021-07-14T04:46:05Z","title":"Real-time dynamics of the $O(4)$ scalar theory within the fRG approach","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.06482","snapshot_observed_at":"2026-08-02T17:48:29.546262Z","title":"Tan, Y.-r","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:29.546262Z"},"links":{"cited_paper":"/paper/2107.06482","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:d2c9d511313d543c23aa0bced70b9e8d13b19426c29879e90fd6e14a08442918","observation_id":"e99bfd6f-d21f-4dcd-bf1a-7831cd5c9b51","resolution":{"observed_at":"2026-08-02T17:48:29.546262Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T17:48:29.672583Z","title":"Hohenberg and B","venue":null,"work_id":null,"year":1977},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:29.672583Z"},"links":{"citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:9c4df1d839054efe84406c692e3beecb7fd3ac2f8facbfc9fab1d227b7e37c57","observation_id":"e2a7c12a-3b41-4b3b-945f-882b26525174","resolution":{"observed_at":"2026-08-02T17:48:29.672583Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T17:48:29.770273Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:29.770273Z"},"links":{"citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:08897f86e53c3e20290773e0e273674079a1001ae3317af17ffef1ab7ab0f198","observation_id":"41fa34f7-43bf-493e-85a0-786c0f10a3db","resolution":{"observed_at":"2026-08-02T17:48:29.770273Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T17:48:29.884530Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:29.884530Z"},"links":{"citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:afbb5d7ed9a9907fc36eb81e7e3d1f63512a879da16a6df7d624045fa84227be","observation_id":"1f58f02e-ce5a-4865-9ae9-faa6b134a5ee","resolution":{"observed_at":"2026-08-02T17:48:29.884530Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T17:48:29.960039Z","title":"Falcon and The PyTorch Lightning team, PyTorch Lightning (2019), version 1.4, software","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:29.960039Z"},"links":{"citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:564ec67be0c246068939dda38de24a39cac1d3ab8acc26c0e3dd03d2a026abe6","observation_id":"5a0c3a51-f4f5-43df-aaee-49d8a894ceff","resolution":{"observed_at":"2026-08-02T17:48:29.960039Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.10249","last_updated":"2023-04-08T03:02:20Z","snapshot_observed_at":"2026-08-14T11:47:02.681685Z","submitted_at":"2022-11-18T14:10:20Z","title":"Criticality of the $O(N)$ universality via global solutions to nonperturbative fixed-point equations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.10249","snapshot_observed_at":"2026-08-02T17:48:30.018286Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:30.018286Z"},"links":{"cited_paper":"/paper/2211.10249","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:7df62c839658ff9e5a3090e50f5796eef6aa449f3e2777b2917d13d90552edcc","observation_id":"0feae9ff-7dc3-4a28-9826-f7686b7c4448","resolution":{"observed_at":"2026-08-02T17:48:30.018286Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T17:48:30.100344Z","title":"Tancik, P","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:30.100344Z"},"links":{"citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:93ecbc9001efdc021709fbf9ed07b5778e1c95dfd74a0fda61867666340e3c5d","observation_id":"b636bc19-ae65-4d8d-a3ce-cd6715987f40","resolution":{"observed_at":"2026-08-02T17:48:30.100344Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T17:48:30.154127Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:30.154127Z"},"links":{"citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:ae03b06acd8ee82c8314fa2b3bbdf5ec3617ec84c4e7d7346a1d498cfc41f655","observation_id":"93b141a3-5e77-46dd-ad27-5b4a5dec5807","resolution":{"observed_at":"2026-08-02T17:48:30.154127Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.08895","last_updated":"2021-05-17T03:12:33Z","snapshot_observed_at":"2026-08-14T20:53:04.124337Z","submitted_at":"2020-10-18T00:34:21Z","title":"Fourier Neural Operator for Parametric Partial Differential Equations","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.08895","snapshot_observed_at":"2026-08-02T17:48:30.232420Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:30.232420Z"},"links":{"cited_paper":"/paper/2010.08895","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:0b5b665080e68017ef46b13529dddd92d3a03c10bb7020823d8d02946d2f37fb","observation_id":"9c483c21-d6e0-46bf-bc47-03328251b43d","resolution":{"observed_at":"2026-08-02T17:48:30.232420Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.13679","last_updated":"2025-08-28T10:21:55Z","snapshot_observed_at":"2026-08-12T22:40:48.857382Z","submitted_at":"2024-09-20T17:41:43Z","title":"Physics-informed renormalisation group flows","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.13679","snapshot_observed_at":"2026-08-02T17:48:30.334673Z","title":"Ihssen and J","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:30.334673Z"},"links":{"cited_paper":"/paper/2409.13679","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:084e1b1c9d18e6d000214cda3c4ea27d79dd002b4eba0e998ab8170728400437","observation_id":"87d29d2e-0531-4bd2-82fc-3b55a692b50f","resolution":{"observed_at":"2026-08-02T17:48:30.334673Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T17:48:30.452376Z","title":"Ihssen, R","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:30.452376Z"},"links":{"citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:819e09577ab3d7403aae1e9ae3876b5abdcc0f79c9726d02dc63d497eeaad988","observation_id":"073e7edf-2f72-45ae-bfac-433b7c0cbd13","resolution":{"observed_at":"2026-08-02T17:48:30.452376Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.13011","last_updated":"2026-04-10T09:19:38Z","snapshot_observed_at":"2026-08-12T21:54:37.403131Z","submitted_at":"2025-07-17T11:31:50Z","title":"Physics-informed operator flows and observables","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.13011","snapshot_observed_at":"2026-08-02T17:48:30.578739Z","title":"Ihssen and J","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:30.578739Z"},"links":{"cited_paper":"/paper/2507.13011","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:f12c11540d3076f1fce3ec7f6cb387e0298daec6e80382b0b54c8bb63cb4c5f3","observation_id":"bb8f0c39-fe98-4a6b-b6c9-3fa63a674e00","resolution":{"observed_at":"2026-08-02T17:48:30.578739Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T17:48:30.643147Z","title":"Ihssen, R","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:30.643147Z"},"links":{"citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:21d65f8f328bacc3a8e328e94181e0e0afa2c53e21379995b6bdc5a2502b7dc2","observation_id":"4a8f49f9-fa8b-40de-a018-7f146746c5f9","resolution":{"observed_at":"2026-08-02T17:48:30.643147Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T17:48:30.745172Z","title":"Tan, W.-j","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:30.745172Z"},"links":{"citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:c143c89eca7dfd2def04ea0b6d46b69f15dab41628efc1078bd28ce528f5817e","observation_id":"589d43ef-d5df-4458-899a-b9fc19515b8c","resolution":{"observed_at":"2026-08-02T17:48:30.745172Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.05870","last_updated":"2023-12-10T12:44:17Z","snapshot_observed_at":"2026-08-13T05:05:44.346119Z","submitted_at":"2023-12-10T12:44:17Z","title":"Critical dynamics within the real-time fRG approach","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.05870","snapshot_observed_at":"2026-08-02T17:48:30.792529Z","title":"Chen, Y.-y","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks","version":2},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-02T17:48:30.792529Z"},"links":{"cited_paper":"/paper/2312.05870","citing_paper":"/paper/2603.21151"},"observation_digest":"sha256:1e801652e9bfaa711866b7de9e50a2e6f32c9b599aa8ce35ba00c22ede5a6cd1","observation_id":"dd394768-eb39-44cb-bf29-a96d905738e3","resolution":{"observed_at":"2026-08-02T17:48:30.792529Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2603.21151","last_updated":"2026-07-27T12:20:59Z","latest_version":2,"primary_category":"hep-ph","snapshot_observed_at":"2026-08-14T23:05:01.577806Z","submitted_at":"2026-03-22T10:03:22Z","title":"Solving Functional Renormalization Group Equations with Neural Networks"},"reference_resolution":{"displayed":83,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":82,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":83},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 83 of 83 outbound references and 1 inbound Pith citation observation for arXiv:2603.21151."}